Numerous systems and related databases for monitoring business performance are known. Methods are also known for analyzing such business performance data having regard to a number of criteria, including for the purpose of data modeling and deriving market strategy data.
Business performance could be measured in terms of a number of such indicia such as sales, market share within a defined market segment, sales increase per dollar spent on items such as marketing, and a broad range of financial performance data, etc.
One of the challenges in providing such systems and related databases, and implementing such methods, is that monitoring of performance of a business may preferably include analysis of relevant data of different types, and further segmentation within such different types of business performance related data. Another challenge in providing such systems and related databases, and implementing such methods, is that businesses are often organized into a number groups for the purpose of business performance, and it may be preferable to measure business performance in accordance with one or more of such groups apart from the rest of the business. Another challenge is that it is often difficult to obtain a holistic or enterprise view of customer behaviour.
For example, to illustrate the organization of businesses into a plurality of groups, many businesses in the financial services, retail and telecommunications areas are organized into separate business units differentiated by geographical area, product line and increasingly, by customer segment. Each separate business unit constitutes a distinct profit center, whereby it is desirable to permit variable compensation from profit center to profit center by measuring each profit center's business performance.
A financial services company provides a useful example. The financial services company offers a wide range of products and services to the retail (individuals) market. Presume that they have only two customers, “A” and “B” and that each has holdings of products or services in two time periods “0” and “1’ as follows:
Customer ACustomer ACustomer BCustomer BPeriod 0 (t0)Period 1 (t1)Period 0 (t0)Period 1 (t1)ProductBalanceBalanceBalanceBalanceDemand10050500600DepositTerm Deposit2000305040004000Money Market3000200030004000Mutual FundTotal5100510075008600In this example, Customer “A” has transferred monies from Demand Deposit and Money Market accounts to fund an increase in Term Deposit balance. Customer “B” has invested an additional 100 in Demand Deposit and 1000 in Money Market accounts.
We will use this example to illustrate the prior art methods that are commonly used to measure business performance, and the disadvantages of such prior art methods, as well as system and computer products that implement such prior art methods. The financial services business is illustrative of the disadvantages of the prior art because financial services businesses generally offer a plurality of “products” in more than one location at any given time. It should be understood that the indicia described below in relation to the prior art methods described below under the headings “Account Level Meaurement”, “Product Portfolio Measurement”, and “Customer Level Measurement” have particular relevance to the example of a financial services business. Such indicia, however, will generally have equivalents in the context of other businesses. For example, product measurement, product line measurement and unit measurement e.g. in retail, Baked beans with no additions, all Beans, one supermarket's Bean sales.
Account Level Measurement
Businesses, including financial services business, will generally engage in “Account Level Measurement” which involves comparing business volume metrics (transaction volume, transaction value, balance value, etc.) at two points in time and calculating the difference between them as a metric of change. This is a commonly used practice, in the public domain, that is quite sufficient for mono-line (single product) businesses and businesses where product substitution is not common behaviour. Identification of changes in business volume in such businesses can be identified by identifying change in business volume (if any) between two given points in time.
Using account level metrics, the following observations will be noted for our illustrative example:
Customer A ChangeCustomer B ChangeProductt0 − t1t0 − t1Demand Deposit−50+100Term Deposit+1050NilMoney Market Mut. Fund−1000+1000Interpretation of this data for each product respectively, as outlined herein, indicates the following:                1. Demand deposit accounts had an outflow of 50 and an inflow of 100        2. Term deposit accounts had an outflow of nil and inflow of 1050        3. Money market funds had an outflow of 1000 and an inflow of 1000.        
Yet we know from the example that this overstates the activity of Customer “A” that merely transferred money between accounts. Therefore, one disadvantage of the known method of measuring business performance in accordance with “Account Level Measurement” is that the data provided as a result of such method does not permit distinction between transfers (“internal”) and third party (“external”) money flows.
“Account Level Measurement” may in some circumstances be deficient as a basis for determining changes in business volume when one product or service can be used by the customer as a substitute for another. In this circumstance, measurement of business volume change at the account level does not necessarily imply a change in business volume at the customer level because one account's volume may decrease due to the substituted use of another product or service. Product substitution may not be easily separated in accordance with prior art “Account Level Measurement” methods from business volume changes associated with business acquired or lost.
In view of the above, “Account Level Measurement” as a basis for monitoring and analyzing business performance may result in a number of disadvantages.
First, the measurement of real business volume changes will be inflated by failing to recognize and eliminate the effects of internal transfers between accounts and products.
A second potential disadvantage of “Account Level Measurement” is that business performance measurements in accordance with this method may indicate success and failure inappropriately, as in the example above where demand deposits “gained” 100, term deposits “gained” 1050 and money market “gained” 1000. Similarly demand deposits “lost” 50, term deposits “lost” nil and money market “lost” 1000. In reality, all of the effects of Customer “A”'s business activity should preferably be removed from the business performance measurement criteria as it was made up entirely of internal transfers.
A third potential disadvantage of “Account Level Measurement” of business performance is that reliance on this method of measurement may result in distortion of compensation of sales personnel based on sales performance. It is a common practice for companies to pay sales forces for acquisition of new business. Reliance on account level metrics results in overcompensation of sales personnel due to the failure to eliminate the effects of internal transfers in the determination of acquisition volume, as outlined above.
A fourth disadvantage of “Account Level Measurement” may result when a business relies on information of this type as input to behavioral models or marketing strategy or actions. False leads are indicated where it appears that a customer is adding or removing business volume when in fact it is simply being transferred internally.
Product Level Measurement
Businesses, including financial services business, will generally also engage in the known method of “Product Level Measurement” or “Product Portfolio Measurement” for monitoring business performance. Specifically “Product Level Measurement” tracking of changes in business volume has been developed as an alternative to “Account Level Measurement” in businesses where product substitution effects are pervasive. “Product Level Measurement” involves comparing business volume metrics (transaction volume, transaction value, balance value, etc.) at two points in time, after aggregation of related accounts based on a cross-reference index that defines an account to product or product group relationship, and calculating the difference between them as a metric of change, as illustrated below. This is a commonly used practice, in the public domain.
Provided below, for purposes of illustration, based on the parameters already provided in the first example above, is a simple example of application of “Product Level Measurement” in the context of a financial services business:
Product ChangeProductt0 − t1Demand Deposit+50Term Deposit+1050Money Market Mut. FundNilInterpretation of this data provides the following information:                1. Demand deposit accounts had an inflow of 50        2. Term deposit accounts had an inflow of 1050        3. Money market funds had no change.        
Yet we recall that Customer “A” has transferred monies from Demand Deposit and Money Market accounts to fund an increase in Term Deposit balance, and Customer “B” has invested an additional 100 in Demand Deposit and 1000 in Money Market accounts.
“Product Level Measurement” may be disadvantageous as a means for monitoring business performance in that it requires aggregation of individual account level data prior to determining whether a change has occurred at the product level. As a direct result of the aggregation process, individual account, customer and customer group behaviour is obscured from the analysis of business volume change.
A consequence of this aforesaid disadvantage may be that changes observed in a product level metric do not “explain” changes that are observed at the customer or customer group level, due to the offsetting effects of changes in individual customers' accounts. In addition, when product or service substitution occurs that crosses between products or product groups at a level of aggregation higher than that chosen for the analysis, the transfer will be interpreted as an external flow instead of a transfer.
For the reasons stated, “Product Level Measurement” may result in a number of other related disadvantages in monitoring business performance.
First, business performance measurements may indicate success and failure inappropriately, as in the example above where demand deposits “gained” 50, term deposits “gained” 1050 and money market was neutral. In reality, all of the effects of Customer “A”'s business activity should be removed from the performance measurement criteria.
A second potential disadvantage in basing business performance monitoring on “Product Level Measurement” occurs when sales compensation plans are based on metrics of this type. As stated earlier, it is a common practice for companies to pay sales forces for acquisition of new business. Placing reliance on product level metrics may result in inappropriate compensation of sales personnel due to the failure to eliminate the effects of internal transfers between products in the determination of acquisition volume.
Customer Level Measurement
Businesses, including financial services businesses, may engage in yet another known method for business performance monitoring, namely “Customer Level Measurement”. This method has been developed as an alternative to account level metrics in companies where product substitution effects are pervasive. “Customer Level Measurement” of changes in business volume involves comparing business volume metrics (transaction volume, transaction value, balance value, etc.) at two points in time, after aggregation of related accounts based on a cross-reference index that defines an account to customer relationship, and calculating the difference between them as a metric of change. This method is illustrated by the example provided below.
Provided below, for purposes of illustration, based on the parameters already provided in the first example above, is a simple example of application of “Customer Level Measurement” in the context of a financial services business:
Customer “A” ChangeCustomer “B” Changet0 − t1t0 − t1Nil1100Interpretation of this data provides the following information:                1. Customer “A” exhibited no change        2. Customer “B” added 1100 in business volume        
Recall that Customer “A” has transferred monies from Demand Deposit and Money Market accounts to fund an increase in Term Deposit balance, and Customer “B” has invested an additional 100 in Demand Deposit and 1000 in Money Market accounts.
“Customer Level Measurement” may be disadvantageous as a means for monitoring business performance in that it requires aggregation of individual account level data prior to determining whether a change has occurred at the customer level. As a direct result of the aggregation process, individual account, product and product group behaviour is obscured from the analysis of business volume change.
A consequence of this aforesaid disadvantage may be that measurement at the customer level provides no insight into which accounts, products or product groups underlying the observed customer level change are affected, limiting the usefulness of the information for the purposes of portfolio analysis, performance planning and evaluation, compensation of identification of specific marketing strategies or actions.
The aforesaid disadvantage is best illustrated with another example. Consider the marketing manager's situation. They know that Customer “A” has not changed their position in the period and Customer “B” has added 1100 to their position. The marketing manager or his/her staff must decide what appropriate communications to direct to each of these customers.
Customer “A” appears to have done nothing, according to the measurement provided by the prior art. In fact, they have changed product use substantially, shifting monies from money market mutual funds and demand deposits into term deposits. This change has several interesting features, none of which are revealed to the marketing manager by the prior art.
First, the customer has not been dormant, they have made an investment decision of some significance. This may indicate a lifestyle change, needs change or change in expectations about future financial needs. Any of these changes may warrant a dialogue between financial services business and its customer.
Second, in the context of the financial services business example, the customer has shifted away from short term (liquid) assets to a longer term asset category (term deposit). This change suggests that the customer's relationship is less at risk of defection than it previously was, as there is a high correlation between liquidity and customer defection. It should be understood that in other business environments, depending on business specific or industry specific factors, similarly relevant data may be read from such business flows.
Third, due to the reduction in liquidity of the customer's position, there may be an opportunity to sell him/her additional products or services to meet short-term financial obligations that may arise, such as for example a credit card or line of credit.
An appropriate response to customer A's behaviour would be a dialogue concerning investment preference, changes in their financial prospects and potential relevance of short term lending products to provide liquidity. The prior art indicated no insight into this dialogue, because the pertinent insights were lost in the aggregation of the customer's product holdings prior to performing the analysis of change in customer position.
Customer “B” in accordance with the example provided has added monies to their accounts. The prior art methods may not detect this increase in position, and may fail to indicate which products increased or whether the accounts were newly opened or augmented. The marketer knows a “thank you” letter is an appropriate response, but the prior art methods may fail to provide the context for such a communication such as what the customer bought.
Financial services business provide a classic example of the downstream effects of the aforementioned “gaps” in business performance data provided by the prior art methods described. Deposit taking business units within financial services businesses suffered substantial decline in business volume throughout the 1990s while mutual funds business grew at a rate well in excess of market growth. Managers of these institutions have in many cases been unable to determine how much of the growth in mutual funds was a result of third party sales and how much was attributable to internal transfers of business. Similarly, managers have not been able to identify how much of the decline in term deposits resulted from transfers to mutual funds (due to changing investor preference) and how much of the business was lost to third parties. Knowing this information is critical to performance management and strategic management of a financial services business.
Similar problems are found wherever product substitutes are available from a business, including for example in the areas of retail sales or telecommunications industries. For example, when introducing a new product into an existing line of similar products in the retail sales industry it is often difficult to establish how much of the new product's sales volume is the result of cannibalization or substitution versus new sales for the product line when considered as a whole.
A different performance management problem arises when a company institutes more than one of the prior art methods at the same time. Because there are offsetting effects within a customer's total position that cancel each other out when a customer transfers business among products or services (some go up some go down), the measurement of business performance at the customer level may not agree with the results, based on the same data, calculated at the product or account levels. This situation arises often and results in confusion and uncertainty among the decision-makers of the business as to which information is correct. The prior art methods do not generally permit the manager to reconcile between the different views of business performance provided by the prior art methods described, i.e. such prior art methods may not provide an overall snapshot of business performance relative to account, product and customer levels simultaneously.
In addition to performance management at an executive and strategic level, sales commissions are also frequently paid to sales persons and agents for bringing new business into a company, as mentioned above. This is a common practice in financial services businesses (excluding brokerage divisions), telecommunications and retail sales businesses. Without the ability to differentiate between “old money” transferring from one product to another and “new money” transferring into the company from external sources, there is a substantial risk of overpayment of commissions. Particularly in areas of business where sales commissions represent a relatively significant cost of business, the resultant overpayment may also be commensurately significant.
It is also well known that this phenomenon is exacerbated by sales persons who may take advantage of the technical aspects of their performance measurement systems in order to maximize their commission earnings. For example, a shrewd sales person may direct a customer to move money into first one product then another and so forth in order to generate a commissioned payment on each transaction executed, to the detriment of the company. The prior art methods do not provide for differentiation of “new” and “old” money, which is fundamental to internal control of commission payments.
Furthermore, the prior art methods generally do not provide key information that is useful in time series analysis of customer behaviour. At the level of the individual customer it is pertinent to be aware of changes in their business activity levels and preferences. Many businesses expend significant resources on acquiring and implementing data mining and business intelligence capabilities, and also on developing predictive models of customer behaviour. Examples of specific customer behaviour of interest includes the propensity of a customer to buy a product based on affinity group, propensity to abandon or defect from a product, propensity to default on a credit instrument and the like. The prior art does include a number of models of this nature, which derive their predictive capability through multivariate linear regression or other mathematical techniques, usually using psychographic, demographic and transactional data as independent variables in the analysis. However, the prior art may not include predictive modeling of customer behaviour based on identified and classified flows of customer business into, out of and among products and services, since the prior art does not enable these actual customer behaviours to be defined, classified and measured on a reliable basis.
The prior art may also generally fail to identify qualified opportunities for specific marketing actions and interventions with customers. It is useful to identify candidates for retention activity based on knowledge that a specific customer is in the process of defection of their business relationship. This proposition is supported by a study conducted by McKinsey & Co. (“Customer retention is not enough”, The McKinsey Quarterly, 2002, Number 2), which indicates that customers engage in a process of defection that takes a considerable period of time. Most of the cost to a business arising from lost business arises through the process of partial defection (removing part of a customer's business) rather than total defection (removal of all of a customer's business). McKinsey cites examples showing the value loss from partial defection in retail banking at 8 times the value of total defection and 7 times in the airline industry. During the process of defection, earlier intervention has much greater chance of success than later intervention. Accordingly, it is highly valuable to identify customers who exhibit defection behaviour as early as possible. The prior art does not identify defection behaviour separate from internal transfers, except when measuring at the total customer position level. Separating out true defection behaviour is imperative to intervention actions to reduce the volume of contact effort and avoid interactions with customers who appear to be defecting but are actually just transferring their business among products or services.
To summarize, the prior art relating to measuring business flows in these industries falls into three categories (a) account level measurement of changes in business volume (b) product level measurement of changes in business volume and (c) customer level measurement of changes in business volume.
In accordance with the examples provided above, the known methods of “Account Level Measurement”, “Product Level Measurement” and “Customer Level Measurement” may not provide adequate means for monitoring business performances, and particularly as a means for producing business performance data that is sufficient for meaningful business performance analysis, or for appropriate response to a business gain or business loss.
Therefore there is a need for a method, system and computer product for monitoring business performance that permits reconciliation of business performance data at the account, customer and product levels.
Specifically, in regard to the desired system and computer product for monitoring business performance that permits reconciliation of business performance data at the account, customer and product levels, it is desirable that such system and computer product be integrated with the information systems of a business in order to provide such business performance data on an efficient and reliable basis.
Systems are known which are capable of providing multiple hierarchies of data and supporting data queries across a selected plurality of data hierarchies, including throughout selected hierarchy levels within such plurality of data hierarchies.
For example, U.S. Pat. No. 6,026,382 issued to Kalthoff on Feb. 15, 2000 discloses a financial system comprising a computer for storing a database describing operations of a financial institution. Significantly, the financial system also comprises a database management system, performed by the computer, which accesses the database stored in the computer via relationships between the different data structures related to the operations of a financial institution, namely party data, product data, account data, internal organization data, contact/transaction event data, location data, campaign data, and channel data.
As another example, U.S. Pat. No. 5,819,251 issued to Kremer et al. on Oct. 6, 1998, discloses a system and apparatus, which stores, retrieves and analyzes relational and non-relational data. An application program provides a data query statement containing both relational and non-relational portions to a relational server. In an embodiment, the query statement is a Structured Query Language (“SQL”) CONTAINS stored procedure or CONTAINS function statement. The relational data server then provides the non-relational query to either a text queue or database management language (“DML”) queue. A non-relational data server then accesses either the text queue or the DML queue. The non-relational data server obtains pointers to the non-relational data and stores them in a temporary table. The pointers and relational data portion are processed by the relational server to obtain the relational and non-relational data. In an embodiment, the non-relational data server is a text server including an engine, filter, lexer, data storage and word list.